Digital marketers and other providers of products and services have a need for identifying the interests of potential customers in order to transmit targeted electronic communications that market the products and services. Tracking entities (which include entities such as advertisers, marketers, and other agencies) often track online actions taken by individuals and other entities on websites and other online services. An individual's online shopping history, for example, is tracked to identify the types of products or services the individual is interested in. A tracking entity then transmits targeted communications to individuals according to the identified interests of the individuals. Efficient and personalized tailoring of the content of the communications increases the responsiveness to products presented to the users.
While tracking entities identify interests of potential customers based on information collected on online activity, there is a need for targeting electronic communications based on frequency of real-world product exposure. For example, as an individual commutes from his or her office to home using a public train or subway, the individual is exposed to a variety of products and services. Other individuals on the train may be wearing smart watches, using a recently released smartphone, or using other accessories that are trending in the market. The next morning, the same individual encounters additional instances in the real-world where the same trending products are in use. By continually encountering the same products and accessories while the individual is engaged in real-world activities (as opposed to online activities such as web browsing and online shopping), the individual's interest level in the products naturally rises. An individual who is continually exposed to a recently released and trending smartphone used by the individual's friends and colleagues would also have increased interest level in the new smartphone.
While tracking entities often devote significant resources to analyzing the online activities of individuals and create a detailed digital marketing profile based on such analysis, there is currently no mechanism for detecting the frequency of real-world product exposure and determining user interests based on the frequency of real-world product exposure. One reason for this is that the individual's offline activities often do not involve direct interaction with a computer system, thus making such offline activities difficult to observe, track, record, and analyze which products an individual is exposed to in the real-world. Computing devices, such as smartphones, may provide some insight into an individual's location and thus provide insight into which products to recommend while the user is visiting that location. For example, a tracking entity may determine that an individual is visiting a casino and then use the tracking information to provide targeted communication marketing playing cards or poker chips. However, such geolocation based product targeting cannot capture an individual's interests based on product exposure outside of targeted geographic venues. An individual that plays games of poker on a weekly basis at a friend's home or takes out playing cards when waiting for food at a restaurant demonstrates a high interest in products such as playing cards and poker chips. However, current platforms and techniques for audience management and digital targeting cannot capture these interests that are indicated through patterns of real-world product exposure.
A digital marketing profile that is based only on online activities and geolocation will be less valuable to a tracking entity who wishes to develop a more comprehensive profile for the individual based on the individual's real-world product exposure. Existing digital marketing profiles therefore omit substantial, and potentially valuable, information from the real-world that would provide insight into an individual's interests. Thus, there is a need for systems that determine comprehensive marketing profiles for individuals based on identified patterns of real-world product exposure.